Skip to main content

726
UPLOADS


Media Type
1
collections
692
movies
33
texts
Year
5
2019
27
2018
55
2017
25
2016
33
2015
51
2014
More right-solid
Topics & Subjects
113
neuroscience
55
theoretical neuroscience
28
brain
28
vegan
28
vegetarian
27
NSF
More right-solid
Collection
More right-solid
Creator
107
redwood center for theoretical neuroscience
51
san francisco vegetarian society
29
2007 brain network dynamics conference
10
organicathlete
8
uc berkeley neurophilosophy group
7
icbs.berkeley.edu
More right-solid
Language
177
English
SHOW DETAILS
up-solid down-solid
eye
Title
Date Archived
Creator
Community Media
texts
eye 0
favorite 0
comment 0
Talk by James Cooke from UCL.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Perception has long been considered to be an inference operation in which internal models of the sensory environment are constructed through experience and are subsequently used in order to assign sensory stimuli to appropriate object categories. When confronted with noisy or incomplete sense data, these internal models are used to identify the most likely object category that might...
Topics: auditory perception, neuroscience
Community Media
texts
eye 11
favorite 0
comment 0
Talk by Stella Yu from ETH Zurich.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Computer vision has advanced rapidly with deep learning, achieving super-human performance on a few recognition benchmarks.  At the core of the state-of-the-art approaches for image classification, object detection, and semantic/instance segmentation is sliding-window classification, engineered for computational efficiency.  Such piecemeal analysis of visual perception often...
Topics: computer vision, machine learning, deep learning
Community Media
texts
eye 21
favorite 0
comment 0
Talk by Yulia Sandamirskaya from ETH Zurich.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Activity of neuronal populations in several cortical regions can be described by a Dynamic Neural Field (DNF) equation. A DNF is a continuous in time and in space activation function defined over a metric space spanned over perceptual (e.g., color, retinal location, orientation) or motor (e.g., orientation of the head, direction of movement) dimensions, in which...
Topics: neural networks, Dynamic Neural Field
Community Media
texts
eye 18
favorite 0
comment 0
Talk by John Co-Reyes from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Sparse reward and long horizon tasks are among the most interesting yet challenging problems to solve in reinforcement learning. I will discuss recent work leveraging representation learning to tackle these sets of problems. We present a novel model which learns a latent representation of low-level skills by embedding trajectories with a variational autoencoder. Skills...
Topic: reinforcement learning
Community Media
texts
eye 18
favorite 0
comment 0
Talk by Carlos Brody from Princeton University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: I will describe studies of the neural bases of cognitive processes. Rodents, mostly rats, are trained to perform behaviors that lend themselves to quantitative modeling that can help identify and assess specific cognitive processes, such as decision-making, short-term memory, planning, and executive control. With these well-quantified behaviors in hand, we then...
Topics: computational neuroscience, decision-making
Community Media
texts
eye 40
favorite 0
comment 0
Talk by Shiming Tang from Peking University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract My lab focuses on the neural mechanism of visual object recognition and developing techniques for neuronal circuit mapping. We have established long-term two-photon imaging in awake monkeys — the first and critical step toward comprehensive circuit mapping — to identify single neuron functions. We have systematically characterized the V1 neuronal responses with...
Topics: visual cortex, V1
Talk by Gamaleldin Elsayed from Google Brain.   Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples...
Topic: machine learning
Community Media
texts
eye 18
favorite 0
comment 0
Talk by Peter Bickel from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: (Joint work with Gil Kur and Boaz Nadler) The notion of projection pursuit according to Huber (1985) appeared first in work of J.B. Kruskal (1969,1972) and was implemented and popularized by Friedman and Tukey (1974).  Key papers are Huber (1985) and Diaconis and Freedman (1984).  The notion, crudely,  according to Huber, is that a p dimensional sample  can be studied...
Topic: dimensionality reduction
Community Media
texts
eye 31
favorite 0
comment 0
Talk by Russ Webb from Apple, Inc.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract A primary goal of this work is to give a clear example of the limits of current, deep-learning techniques and suggest how progress can be made.  The presentation will include a discussion of open questions, unpublished experiments, suggestions on how to make progress. This work is founded on the paper Knowledge Matters: Importance of Prior Information for Optimization by...
Topics: machine learning, neural networks
Community Media
texts
eye 39
favorite 0
comment 0
Talk by Aviv Tamar who is a postdoc at UC Berkeley’s Artificial Intelligence Research lab.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract How can we build autonomous robots that operate in unstructured and dynamic environments such as homes or hospitals? This problem has been investigated under several disciplines, including planning (motion planning, task planning, etc.), and reinforcement learning. While both of these fields have witnessed tremendous...
Topics: Reinforcement Learning, planning, robotics, AI
Community Media
Sep 17, 2018
texts
eye 39
favorite 0
comment 0
Talk by Juergen Jost from MPI for Mathematics in the Sciences, Leipzig.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract   In many situations, two or more sources have some information about a target. For instance, sensory input and context information can jointly determine the firing pattern of a neuron. Since the information from the two sources is typically not identical, one wishes to decompose it in those parts that are unique to each source, what is...
Topic: Information theory
Community Media
Jul 20, 2018
texts
eye 23
favorite 0
comment 0
Talk by Tori Sharma, PhD, of UC Berkeley.  Given at the 2018 Berkeley course in mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: research ethics, responsible research
Community Media
texts
eye 30
favorite 0
comment 0
Talk by David Foster of UC Berkeley.  Given to the Berkeley course in mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: hippocampus, place cells
Lectures given by Maneesh Sahani of UCL in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: PCA, factor analysis, computational neuroscience
Talk by Christoph Schreiner of UCSF.  Given to the Berkeley course on  mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: auditory cortex, neuroscience
Community Media
texts
eye 32
favorite 0
comment 0
Talk by Dan Feldman of UC Berkeley.  Given to the Berkeley course on mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topic: sensory cortex
Lectures given by Stephanie Palmer for the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topic: spike sorting
Community Media
texts
eye 32
favorite 0
comment 0
Talk by Michael Silver of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July9-20 at UC Berkeley.
Topics: brain activity, human vision, spatial attention
Lectures given by Odelia Schwartz for the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: image statistics, normative models, vision, theoretical neuroscience
Community Video
movies
eye 14
favorite 0
comment 0
Videos of the lectures given by David Sussillo of Google in the 2018 Berkeley course in mining and modeling neuroscience data are NOT publicly available.  However, a video of a lecture he gave that includes the same content of his main lecture in the course is at: https://simons.berkeley.edu/talks/david-sussillo-3-22-18 (abstract and video) and: https://www.youtube.com/watch?v=08xa7bE5iTQ&feature=youtu.be (video only). Also, the lab exercise David gave in the course is available in the...
Topics: neural networks, neuroscience, LFADS
Community Media
texts
eye 28
favorite 1
comment 0
Talk by Marla Feller of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 9-20 at UC Berkeley.
Topics: retina development, computational modeling
Lectures given by Frederic Theunissen of UC Berkeley in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: data analysis, neuroscience
Talk by Talk by Michael Yartsev of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 9-20 at UC Berkeley.
Topics: bats, language, learning
Community Media
texts
eye 310
favorite 0
comment 0
Lectures given by Robert Kass from Carnegie Mellon University in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: computational neurosciece, course
Talk by Reza Abbasi-Asl of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In the past decade, research in machine learning has been exceedingly focused on the development of algorithms and models with remarkably high predictive capabilities. Models such as convolutional neural networks (CNNs) have achieved state-of-the-art predictive performance for many tasks in computer vision, autonomous driving, and transfer learning in areas such as...
Topics: Area v4, vision, deep learning
Community Media
texts
eye 49
favorite 0
comment 0
Talk by from UT Austin and The Simons Institute at UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Recent results in information theory show how error-correcting codes on large, sparse graphs (“expander graphs”) can leverage multiple weak constraints to produce near-optimal performance. I will demonstrate a mapping between these error-correcting codes and canonical models of neural memory ("Hopfield networks"), and use expander code...
Topics: neural networks, memory, hopfield networks
Talk by Laurenz Wiskott from the Institut für Neuroinformatik (Ruhr Universität Bochum).  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract    Slow feature analysis (SFA) is a biologically motivated algorithm for extracting slowly varying features from a quickly varying signal and has proven to be a powerful general-purpose preprocessing method for spatio-temporal data in brain modeling as well as technical applications.  We have applied SFA to the learning...
Topics: learning, theoretical neuroscience
Community Media
Apr 6, 2018
texts
eye 99
favorite 0
comment 0
Talk by Sophie Deneve of the University of Paris/Simons Institute.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract The brain computes with spikes, and spikes are costly to metabolism. Yet, the spiking responses of most cortical cells appear extremely noisy, to the extent that the only feature that repeats from trial to trial is the firing rate (and only for the small minority of cells that are modulated at all). If that was the case, the most sophisticated of...
Topic: Theoretical Neuroscience
Talk by Yasaman Bahri of Google Brain.  Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Obtaining a better understanding of neural networks with random parameters is relevant for deep learning practice — for instance, by informing good initializations — and is a natural first step in building a more complete base of knowledge within deep learning. I will survey some of our recent work at Google Brain which originated from the study of random neural...
Topics: deep neural networks, neural networks, deep learning
Talk by Arturo Deza of UC Santa Barbara.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Are there any benefits in incorporating the foveated nature of human vision into image-based metrics of perception and computer vision systems? In this talk I hope to advance our understanding of this question through my work via psychophysical experiments (eye-tracking), computational modelling, and computer vision. The first part of the talk will revolve around...
Topics: human vision, computer vision, fovea
Talk by Leenoy Meshulam from Princeton University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Recent technological progress has dramatically increased our access to the neural activity underlying memory-related tasks. These complex high-dimensional data call for theories that allow us to identify signatures of collective activity in the networks that are crucial for the emergence of cognitive functions. As an example, we study the neural activity in...
Topics: neuroscience, data analysis
Community Media
texts
eye 53
favorite 1
comment 0
Talk by Joe Makin, UCSF.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract. A common task facing computational scientists and, arguably, the brains of primates more generally is to construct models for data, particularly ones that invoke latent variables. Although it is often natural to identify the latent variables of such a model with the true unobserved variables in the world, the correspondence between the two can be more complicated, as when the former are...
Topics: theoretical neuroscience, statistics, density estimation
Talk by Miguel Lázaro-Gredilla of Vicarious, Inc.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkelely. Abstract: Compositionality, generalization, and learning from a few examples are among the hallmarks of human intelligence. In this talk I will describe how Vicarious combines these ideas to create approaches to CAPTCHA breaking and Atari game playing that improve on the state of the art. Both of these tasks have indeed been tackled before, using respectively...
Topics: AI, machine learning, Recursive Cortical Network, Schema Networks
Community Video
movies
eye 101
favorite 0
comment 0
Talk by Zhaoping Li of the University College London.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract. I will present a review of the role of the primary visual cortex V1 in the functions of looking and seeing in vision. Looking is attentional selection, to select a fraction of visual inputs into the attentional bottleneck for deeper processing. Seeing is to infer or decode the properties of the selected visual inputs, e.g., to recognize a face. In...
Topics: vision, visual cortex
Lecture 13 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Christof Koch, of the Allen Institute for Brain Science. Abstract from a recent paper by the speaker: There have been a number of advances in the search for the neural correlates of consciousness — the minimum neural mechanisms sufficient for any one specific conscious percept. In this Review, we describe recent findings showing that the...
Topic: consciousness
Community Video
movies
eye 57
favorite 0
comment 0
Talk by Joel Kaardal from the Sharpe lab at the Salk Institute.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Characterizing the computations performed by high-level sensory regions of the brain remains enigmatic due to the many nonlinear signal transformations that separate the input sensory stimuli from the neural responses. In order to produce interpretable models of these computations, dimensionality reduction techniques can be employed to obtain a...
Topic: computational neuroscience
Talk by Jeff Hawkins of Numenta.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In this talk I will describe a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. The second input is an allocentric representation, which we propose is derived in the sub-granular layers of each cortical column. The allocentric location represents where the sensed feature is relative to the...
Topics: neocortex, cortex, neuroscience
Lecture 11 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Brian Odegaard of UCLA. The abstract of the Odegaard, Knight, Lau paper is the following: "Is activity in prefrontal cortex (PFC) critical for conscious perception? Major theories of consciousness make distinct predictions about the role of PFC, providing an opportunity to arbitrate between these views empirically. Here we address three common...
Topics: consciousness, neuroscience
Community Video
movies
eye 109
favorite 0
comment 0
Talk by John Harte of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Constrained maximization of information entropy yields least biased probability distributions. In statistical physics, this powerful inference method yields classical thermodynamics under the constraints implied by conservation laws.  Here we apply this method to ecology, starting with logically necessary constraints formed from ratios of ecological state variables, and derive...
Topics: maximum enropy, ecology
Community Video
movies
eye 18
favorite 0
comment 0
Lecture 11 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Shin Shimojo, of CalTech Abstract There are a few postdictive perceptual phenomena known, in which a stimulus presented later seems causally to affect the percept of another stimulus presented earlier. While backward masking provides a classical example, the flash lag effect stimulates theorists with a variety of intriguing findings. The...
Topic: Consciousness
Community Video
movies
eye 60
favorite 0
comment 0
Lecture 10 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Michael Cohen, formerly at Harvard University, now at Amherst.
Topics: Consciousness, attention, vision
Talk by Caleb Kalmere of Rice University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Topics: hippocampus, replay, memory
Community Video
movies
eye 30
favorite 1
comment 0
Lecture 9 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Ken Nakayama, formerly at Harvard University.
Topics: consciousness, attention
Community Video
movies
eye 17
favorite 0
comment 0
Lecture 8 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Stan Klein and Jerome Feldman, both at UC Berkeley.
Topics: vision, Psychophysics, perception
Community Video
movies
eye 168
favorite 0
comment 0
Talk by Deepak Pathak of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent’s ability to predict the consequence of its own...
Topics: learning, AI
Lecture 7 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Terry Regier and Rich Ivry, both at UC Berkeley.
Topics: language, thought
Community Video
movies
eye 37
favorite 0
comment 0
Lecture 6 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Bruno Olshausen of UC Berkeley.
Topics: AI, theoretical neuroscience, vision, computational neuroscience
Community Video
movies
eye 29
favorite 0
comment 0
Lecture 5 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity."  Lecture given by Ken Nakayama, formerly at Harvard.
Topics: consciousness, vision
Community Video
movies
eye 18
favorite 0
comment 0
Lecture 4 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity".  Lecture given by Peter Tse of Harvard University. Note: there are two versions of the video: the first was recorded by a camera in the room in Berkeley, the second (with "_zoom" in the name) was recorded from the zoom application that was used to stream the talk from Peter's office in Harvard to the room in Berkeley.  The first version includes youtube...
Topics: attention, preconscious operations, consciousness
Lecture 3 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity".  Lecture given by Jerry Feldman of UC Berkeley.
Topics: binding problem, neuroscience, Consciousness
Community Video
Sep 6, 2017 Redwood Center for Theoretical Neuroscience
movies
eye 98
favorite 0
comment 0
Talk by Gerald Friedland of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In this talk, we derive the calculation of two critical numbers that quantify the capabilities of artificial neural networks with gating functions, such as sign, sigmoid, or rectified linear units. First, we derive the calculation of the upper limit Vapnik-Chervonenkis dimension of a network with binary output layer, which is the theoretical limit for perfect fitting of...
Topics: artificial neural network, information theory
Community Video
movies
eye 17
favorite 0
comment 0
Lecture 2 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity".  Lecture given by Stan Klein of UC Berkeley.
Topics: quantum mechanics, consciousness
Community Video
movies
eye 40
favorite 0
comment 0
Lecture 1 of course vision science 298-3 Fall 2017 at UC Berkeley.   Title of course is "Science and Subjectivity".  Lecture given by Stan Klein of UC Berkeley. Description of course: Subjectivity, aka 1st person experience,  is an aspect of the famous mind/body problem that is at least partially tractable. The course will explore a wide variety of past and future experiments for learning about the neural and embodied basis for subjectivity. Each week we will have a paper or two...
Topic: consciousness
Community Video
Aug 28, 2017
movies
eye 48
favorite 0
comment 0
Talk by David Zipser at the lab meeting for the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract POV I will describe a virtual neuroanatomy in which the brain with its neurons is mapped to a new shape homologous to the external space it represents. This mapping preserves all the connectivity and function of the brain. The ‘facts’ remain the same—only the way we view them changes radically.
Topics: neuroscience, neuroanatomy
Talk by Joshua Vogelstein of Johns Hopkins University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Determining whether certain properties are related to other properties is fundamental to scientific discovery. As data collection rates accelerate, it is becoming increasingly difficult yet ever more important to determine whether one property of data (e.g., cloud density) is related to another (e.g., grass wetness). Only if two properties are related are...
Topics: data analysis, correlation
Talks by Ken Miller of Columbia University and Brent Doiron of the University of Pittsburgh.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstracts. Talk by Miller: The Stabilized Supralinear Network, or, The Importance of Being Loosely Balanced I will describe the Stabilized Supralinear Network mechanism and its application to understanding sensory cortical behavior. The mechanism is based on a network of excitatory (E) and inhibitory (I) neurons with very simple...
Topics: cortical circuits, cortex
Community Video
movies
eye 54
favorite 0
comment 0
Talk by Dan Feldman of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 10-21 at UC Berkeley.
Topics: somatosensory cortex, neural coding
Community Video
movies
eye 137
favorite 0
comment 0
Lectures given by Maneesh_Sahani of UCL in the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topic: neural coding
Community Video
movies
eye 72
favorite 0
comment 0
Talk by Talk by Michael Yartsev of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 10-21 at UC Berkeley.
Topics: bats, hippocampus, learning
Talk by Christoph Schreiner of UCSF.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 10-21 at UC Berkeley.
Topics: auditory cortex, neuroscience
Tlak by Jordi Puigbò, Universitat Pompeu Fabra (Barcelona - Spain).  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum, and the amygdala. A particular case in point is the learning paradigm of...
Topics: neocortex, computational model
Community Video
movies
eye 109
favorite 0
comment 0
Lectures given by Mark Goldman of UC Davis in the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topics: neural networks, computational modeling
Community Video
movies
eye 55
favorite 0
comment 0
Talk by Marla Feller of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 10-21 at UC Berkeley.
Topics: retina development, computational modeling
Community Video
movies
eye 124
favorite 0
comment 0
Lectures given by Odelia Schwartz for the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topic: image statistics
Community Video
movies
eye 48
favorite 0
comment 0
Talk by Victoria Sharma, PhD.  Given to the 2017 Berkeley course in mining and modeling of neuroscience data.  This lecture is instruction in the Responsible Conduct of Research (RCR).
Topic: research ethics
Community Video
movies
eye 205
favorite 0
comment 0
Lectures given by Jonathan Pillow of UC Berkeley in the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topics: Generalized Linear Models, theoretical neuroscience
Community Video
movies
eye 107
favorite 0
comment 0
Talk by Rich Ivry of the Cognition and Action Lab at the Univ. of California Berkeley.  Given to the 2017 course in mining and modeling of neuroscience data at UC Berkeley.
Topics: crebellum, movements, theoretical neuroscience
Community Video
movies
eye 50
favorite 0
comment 0
Talk by Michael Silver of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 10-21 at UC Berkeley.
Topics: visual perception, brain activity, human vision
Community Video
Jul 12, 2017
movies
eye 48
favorite 0
comment 0
This is a short video taken of a young Peregrine falcon on the south end of the balcony area outside of the rooms on the 10th floor of Evans Hall at UC Berkeley.  The video was taken on July 11, 2017 about 5:50PM before a talk that was starting at 6PM.  Early during the talk, the falcon made some very loud screeching sounds, but they did not last very long.  After the talk, at about 7:30, the falcon seemed to be OK.  The falcon was not watched for about 30 minutes while we went to find a...
Topics: falcon, evans hall, uc berkeley
Lectures given by Frederic Theunissen of UC Berkeley in the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topics: data analysis, neuroscience
Community Video
movies
eye 470
favorite 0
comment 0
Lectures given by Robert Kass from Carnegie Mellon University in the 2017 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 10-21, 2017 at UC Berkeley in Berkeley California.
Topics: computional neuroscience, course
Talk by David Field from Cornell University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley and also to the 2017 course in mining and modeling neuroscience data (course described at crcns.org). Abstract I will discuss some implications of an approach that attempts to describe the various non-linearities of neurons in the visual pathway using a geometric framework. This approach will be used to make a distinction between selectivity and hyper-selectivity. Selectivity...
Topics: neuroscience, receptive fields
Community Video
movies
eye 130
favorite 0
comment 0
Talk by Pentti Kanerva at the Redwood Center for Theoretical Neuroscience lab meeting. ABSTRACT Traditional computing is deterministic. It is based on bits and assumes that the bits compute perfectly. However, (near) perfection at high speeds consumes large amounts of energy and limits the scaling-down of the underlying circuit elements. Contrast this with the brain's powers of perception and learning. They go far beyond what computers can do, are accomplished with little energy by slow and...
Topic: Theoretical Neuroscience
Talk by Alex Terekhov, of Paris Descartes University ERC "FEEL", and a visiting researcher at the Redwood Center for Theoretical Neuroscience.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Signs of space I will talk about how naive agent can learn the notion of space ex-nihilo. I am particularly interested in what hints space gives to the agent by constraining the agent's sensorimotor flow. But I will start by talking about my attempts to fight...
Topics: neural networks, learning
Community Video
movies
eye 52
favorite 0
comment 0
Talk by Jasmine Collins of Google.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety...
Topics: neural networks, learning
Community Video
movies
eye 92
favorite 0
comment 0
Talk by Saurabh Gupta from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.   Abstract We introduce a novel neural architecture for navigation in novel environments that learns a cognitive map from first person viewpoints and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of...
Topics: AI, planning, navigation
Talk by Pierre Sermanet, from Google Brain.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract We propose a self-supervised approach for learning representations entirely from unlabeled videos recorded from multiple viewpoints. This is particularly relevant to robotic imitation learning, which requires a viewpoint-invariant understanding of the relationships between humans and their environment, including object interactions, attributes and body pose. We train...
Topics: machine learning, computer vision, deep learning
Community Video
movies
eye 27
favorite 0
comment 0
Talk by Dibyendu Mandal at the lab meeting for the Redwood Center for Theoretical Neuroscience at UC Berkeley. ABSTRACT: Active biological systems reside far from equilibrium, dissipating heat even in their steady state, thus requiring an extension of conventional equilibrium thermodynamics and statistical mechanics.  In this Letter, we have extended the emerging framework of stochastic thermodynamics to active  matter. In particular, for the active Ornstein-Uhlenbeck model, we have provided...
Topics: stochastic thermodynamics, biological systems, statistical mechanics
Community Video
movies
eye 15
favorite 0
comment 0
Talk by Saeed Saremi at the lab meeting for the Redwood Center for Theoretical Neuroscience at UC Berkeley.  Subject is based on a paper in the August, 1979 issue of Scientific America by Kenneth G. Wilson titled "Problems in Physics with many Scales of Length".
Topics: size scales, physics
Lectures given by Odelia Schwarts from the University of Miami, in the 2016 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 11-22,  2016 at UC Berkeley in Berkeley California.  Information about the course is at: http://crcns.org/previous-courses/2016_course
Topics: statistics, neuroscience, data
Lectures given by Joshua Volgelstein from Johns Hopkins University, in the 2016 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 11-22,  2016 at UC Berkeley in Berkeley California.  Information about the course is at: http://crcns.org/previous-courses/2016_course
Topics: statistics, neuroscience, data
Community Video
movies
eye 30
favorite 0
comment 0
Lectures given by Robert Kass from Carnegie Mellon University in the 2016 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 11-22, 2016 at UC Berkeley in Berkeley California.  Information about the course is at: http://crcns.org/previous-courses/2016_course
Topics: statistics, neuroscience, data
Lectures given by Frederic Theunissen of UC Berkeley in the 2016 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 11-22, 2016 at UC Berkeley in Berkeley California.  Information about the course is at: http://crcns.org/previous-courses/2016_course
Topics: statistics, neuroscience, data
Community Video
movies
eye 114
favorite 0
comment 0
Lectures given by Maneesh Sahani in the 2016 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 11-22, 2016 at UC Berkeley in Berkeley California.  Information about the course is at: http://crcns.org/previous-courses/2016_course
Topics: statistics, neuroscience, data
Talk by Michael Frank of Magicore Systems.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Technology scaling had been carrying computer science thru the second half of the 20th century until single CPU performance started leveling off, after which multi- and many-core processors, including GPUs, emerged as the substrate for high performance computing. Mobile market implementations followed this trend and today you might be carrying a phone with more than 16...
Topic: parallel computing
Community Video
movies
eye 317
favorite 0
comment 0
Talk by Chris Hillar, former Post-Doc in the Redwood Center for Theoretical Neuroscience at UC Berkeley.  Given at the Redwood Center lab meeting. Title:   Elements of Theoretical Neural Science Abstract:   I will present 4 topics from the theory of brain computation:  Memory/Encoding, Invariance, Behavioral Rhythms, and Language.  Each topic incorporates a particular mathematical model of the relevant processing:  Discrete Recurrent Neural Nets (DRNNs) / Rate-Distortion Theory, The...
Topic: Theoretical Neural Science
Talk by Jozsef Fiser, of the Dept. of Cognitive Science, Central European University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract/ The notion of interpreting cortical operations as probabilistic computation has been steadily gaining ground in neuroscience, and with the emergence of the PPC-based and sampling-based frameworks, now there exist clear theoretical alternatives of how such computation might happen in the brain. Nevertheless, a number of crucial...
Topic: visual cortex
Community Video
movies
eye 70
favorite 0
comment 0
Talk by Sahar Akram of the Starkey Hearing Research Center.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In...
Topics: hearing, auditory attention
Community Video
Feb 24, 2017
movies
eye 14
favorite 0
comment 0
Tutorial about how to use the Redwood Center cluster.  Given by Chris Warner of the Redwood Center for Theoretical Neuroscience.
Topic: computer clusters
Community Video
movies
eye 555
favorite 0
comment 0
Talk by Amir Khosrowshahi, Ph.D., Vice President Intel Corporation.  Given at the UC Berkeley Vision Science OXYOPIA lecture series. Abstract: Deep learning is now state-of-the-art across a wide variety of machine learning domains including speech, video, and text. Nervana is a startup providing deep learning as a platform. Nervana’s core technology is a novel processor architecture for deep learning providing an enabling increase in speed, scale, and efficiency. I will talk about some of...
Topic: deep learning
Community Video
movies
eye 341
favorite 0
comment 0
Talk by Christoph von der Malsburg of the Frankfurt Institute for Advanced Studies and Platonite AG.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Let's go for the real thing: Every waking second, our mind represents for us the situation we are immersed in. How do the physical states of our brain create this mental imagery? I call this the Neural Code Issue. The pathway to solving it is currently blocked by an answer that isn't even wrong. Come to my talk...
Topic: neural coding
Community Video
movies
eye 43
favorite 0
comment 0
Talk by Gautam Agarwal of the Champalimaud Neuroscience Program, Portugal.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Decision-making has been modeled in great detail based on 2-alternative choice (2AC) tasks; however it remains unclear how these models apply to more naturalistic settings, where choices can have long-term and diverse consequences. In turn, quantitatively modeling more complex decisions poses a challenge, requiring adequate sampling of...
Topics: planning, decision-making
Talk by Felix Wichmann of the University of Tübingen.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract. Early visual processing has been studied extensively over the last decades. From these studies a relatively standard model emerged of the first steps in visual processing. However, most implementations of the standard model cannot take arbitrary images as input, but only the typical grating stimuli used in many of the early vision experiments. I will...
Topics: Vision, deep learning
Community Video
Feb 3, 2017
movies
eye 118
favorite 0
comment 0
Talk by Jesse Livezay and Dylan Paiton of the Redwood Center for theoretical Neuroscience. Announcement: We'll be talking about the programming model that Tensorflow (and Theano) uses and then go through a basic logistic regression model. Try installing Tensorflow before you arrive: https://www. tensorflow .org/get_started/os_setup If you use Anaconda python, you can follow the instructions here: https://www. tensorflow .org/get_started/os_setup#anaconda_installation This workshop assumes a...
Topic: Tensorflow
Community Video
movies
eye 83
favorite 0
comment 0
Talk by Marcus Rohrbach from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Language is the most important channel for humans to communicate about what they see. To allow an intelligent system to effectively communicate with humans it is thus important to enable it to relate information in words and sentences with the visual world. One component in a successful communication is the ability to answer natural language questions about the visual...
Topics: deep learning, vision, language, artificial Intelligence
Community Video
movies
eye 98
favorite 0
comment 0
Talk by Pulkit Agrawal of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract The ability to forecast how different objects will be affected by the applied action (i.e. intuitive physics), is likely to be very useful for executing novel manipulation tasks. Similarly, the ability to forecast how humans will act in the future (i.e. intuitive behavior) can enable an agent to plan its interactions with humans. I will present results of some preliminary...
Topics: machine learning, robots
Community Video
movies
eye 166
favorite 0
comment 0
Talk by Karl Zipser of UC Berkeley.  Given to the lab meeting of the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Safe driving requires correct decision-making in the event of the unexpected. A system trained to follow the 'rules of the road' may mimic human driving behavior under some conditions (Koutnik et al., 2013; Chen et al., 2015). However, under unusual conditions that pose safety challenges, the human driver can revert to core evolved behavior for locomotion on...
Topics: self driving car, deep learning, neural networks
Community Video
movies
eye 971
favorite 0
comment 0
Talk by Eric Jonas from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Topic: theoretical neuroscience
Community Video
movies
eye 105
favorite 0
comment 0
Talk by Douglas Jones from the ECE Department, University of Illinois at Urbana-Champaign.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Evolutionary pressure suggests that the spike-based code in the sensory nervous system should satisfy two opposing constraints: 1) minimize signal distortion in the encoding process (i.e., maintain fidelity) by keeping the average spike rate as high as possible, and 2) minimize the metabolic load on the neuron by keeping...
Topics: neuroscience, neural code
Community Video
movies
eye 409
favorite 0
comment 0
Talk by Taco Cohen from the University of Amsterdam.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract The Group equivariant Convolutional Neural Network (G-CNN) is a new kind of neural network that obtains better sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers. G-convolutions increase the expressive capacity of the network...
Topic: Convolutional neural networks